The growing complexity and higher time-to-market pressure make the functional verification of modern large scale hardware systems more challenging. These challenges bring the requirement of a high quality testbench that is capable of thoroughly verifying the design. To reveal a bug, the testbench needs to activate it by stimulus, propagate the erroneous behaviors to some checked points, and detect it at these checked points by checkers. However, current dominant verification approaches focus only on the activation aspect using a coverage model which is not qualified and ignore the propagation and detection aspects. Using a new metric, this paper qualifies the testbench by mutation analysis technique with the consideration of the quality of the stimulus, the coverage model, and the checkers. Then the testbench is iteratively refined according to the qualification feedback. We have conducted experiments on two designs of different scales to demonstrate the effectiveness of the proposed method in improving the quality of the testbench.

Energy optimization for periodic applications running on safety/time-critical time-triggered multiprocessor systems has been studied recently. An interesting feature of the applications on the systems is that some tasks are strictly periodic while others are non-strictly periodic, i.e., the start time interval between any two successive instances of the same task is not fixed as long as task deadlines can be met. Energy-efficient scheduling of such applications on the systems has, however, been rarely investigated. In this paper, we focus on the problem of static scheduling multiple periodic applications consisting of both strictly and non-strictly periodic tasks on safety/time-critical time-triggered multiprocessor systems for energy minimization. The challenge of the problem is that both strictly and non-strictly periodic tasks must be intelligently addressed in scheduling to optimize energy consumption. We introduce a new practical task model to characterize the unique feature of specific tasks, and formulate the energy-efficient scheduling problem based on the model. Then, an improved Mixed Integer Linear Programming (MILP) method is proposed to obtain the optimal scheduling solution by considering strict and non-strict periodicity of the specific tasks. To decrease the high complexity of MILP, we also develop a heuristic algorithm to efficiently find a high-quality solution in reasonable time. Extensive evaluation results demonstrate the proposed MILP and heuristic methods can on average achieve about 14.21% and 13.76% energy-savings respectively compared with existing work.

Energy optimization plays an increasingly critical role in designing an embedded real-time multiprocessor System on Chip (MPSoC). Dynamic Voltage Frequency Scaling (DVFS) and Dynamic Power Management (DPM) are preferable techniques to optimize energy consumption. However, previous DVFS and DPM algorithms were mostly designed for inter-task scheduling, without sufficient exploration on intra-task scheduling for further energy reduction. This paper presents a new intra-task scheduling approach considering the probabilistic distribution of task execution time, and it optimizes the mathematical expectation of power consumption (expected power consumption) for periodic dependent tasks with uncertain execution time running on MPSoCs using DVFS and DPM. The energy-efficient scheduling problem can be formulated by means of mixed integer linear programming (MILP) with the proposed technique. Moreover, we also propose a technique to compress the exploration space by reorganizing the probabilistic profiling information of all tasks. Our experimental results on synthetic and realistic benchmarks show that the proposed approach achieves up to 30% energy savings compared with other existing methods.

Autonomous surgery involves surgical tasks performed by a robot with minimal or no human involvement. Thanks to its precise automation, surgical robotics offers significant benefits in enhancing the consistency, safety, and quality of procedures, driving its growing popularity. However, ensuring the safety of autonomous surgical robotic systems remains a significant challenge. To address this, we propose a simulation-based validation method to detect potential safety issues in the software of surgical robotic systems, complemented by a digital twin to estimate the gap between simulation and reality. The validation framework consists of a test case generator and a monitor for validating properties and evaluating the performance of the robotic system during test execution. Using a robotic arm for needle insertion as a case study, we present a systematic test case generation method that ensures effective coverage measurement for a three-dimensional, irregular model. Since no simulation can perfectly replicate reality due to differences in sensing and actuation, the digital twin bridges the gap between simulation and the physical robotic arm. This integration enables us to assess the discrepancy between virtual simulations and real-world operations by verifying whether the data from the simulation accurately predicts real-world outcomes. Through extensive experimentation, we identified several flaws in the robotic software. Co-simulation within the digital twin framework has highlighted these discrepancies that should be considered.